However, the explainability power of DNN prediction mechanisms has been elusive [1]}.
Thus, researchers have shown interest in correlating DNN predictions to the variability of machine learning input [2]}.
Class activation mapping estimates the contributions of input data by relying on the CNN’s receptive field [3]}.
The temporal LSTM with attention also interprets the time series by employing an attention layer to an RNN structure [4]}.
Layer-wise relevance propagation suggests the possibility of interpreting DNN results by backtracking the values contributing to the prediction results in the NN graph [5]}.
The local-interpretable-model-agnostic-explanations model interprets a prediction using hypothetical data close to the predicted data [6]}.
Meanwhile, GAMs make predictions using contributions extracted by a simple function for each variable in the data [7]}.
The GAM function is presented in Eq. REF . The GAM uses the base module \(f\)  that only focuses on one of the \(K\)  variables for explainable predicting.
Because the GAM employs an isolated module \(f\)  for each variable, the GAM provides the influence of each variable for predicting the target \(\hat{y}\) .
\(\begin{split}\hat{y} =& \beta + f_1(x_1) + \cdots + f_K(x_K) = \beta + \sum _{k = 1}^{K} f_k(x_k)\end{split}\) 
